The border support rank of two-by-two matrix multiplication is seven

M. Bläser, M. Christandl, Jeroen Zuiddam
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引用次数: 5

Abstract

We show that the border support rank of the tensor corresponding to two-by-two matrix multiplication is seven over the complex numbers. We do this by constructing two polynomials that vanish on all complex tensors with format four-by-four-by-four and border rank at most six, but that do not vanish simultaneously on any tensor with the same support as the two-by-two matrix multiplication tensor. This extends the work of Hauenstein, Ikenmeyer, and Landsberg. We also give two proofs that the support rank of the two-by-two matrix multiplication tensor is seven over any field: one proof using a result of De Groote saying that the decomposition of this tensor is unique up to sandwiching, and another proof via the substitution method. These results answer a question asked by Cohn and Umans. Studying the border support rank of the matrix multiplication tensor is relevant for the design of matrix multiplication algorithms, because upper bounds on the border support rank of the matrix multiplication tensor lead to upper bounds on the computational complexity of matrix multiplication, via a construction of Cohn and Umans. Moreover, support rank has applications in quantum communication complexity.
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2 × 2矩阵乘法的边界支持秩为7
我们证明了对应于2乘2矩阵乘法的张量的边界支持秩是7除以复数。我们通过构造两个多项式来实现这一点,这两个多项式在所有格式为4乘4乘4且边界秩最多为6的复张量上消失,但它们不会同时在与2乘2矩阵乘法张量具有相同支持的任何张量上消失。这扩展了豪恩斯坦、伊肯迈耶和兰茨伯格的工作。我们还给出了2 × 2矩阵乘法张量在任意域上的支持秩为7的两个证明:一个是用De Groote的结果证明的,即这个张量的分解直到三明治都是唯一的;另一个是用替换法证明的。这些结果回答了Cohn和human提出的一个问题。研究矩阵乘法张量的边界支持秩与矩阵乘法算法的设计有关,因为通过Cohn和human的构造,矩阵乘法张量的边界支持秩的上界会导致矩阵乘法计算复杂度的上界。此外,支持等级在量子通信复杂性中也有应用。
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